def __call__(self, batch):
not_terminal = 1.0 - batch.terminal.float()
# normalize actions
action = rescale_actions(
batch.action,
new_min=self.train_low,
new_max=self.train_high,
prev_min=self.action_low,
prev_max=self.action_high,
)
# only normalize non-terminal
non_terminal_indices = (batch.terminal == 0).squeeze(1)
next_action = torch.zeros_like(action)
next_action[non_terminal_indices] = rescale_actions(
batch.next_action[non_terminal_indices],
new_min=self.train_low,
new_max=self.train_high,
prev_min=self.action_low,
prev_max=self.action_high,
)
return rlt.PolicyNetworkInput(
state=rlt.FeatureData(float_features=batch.state),
action=rlt.FeatureData(float_features=action),
next_state=rlt.FeatureData(float_features=batch.next_state),
next_action=rlt.FeatureData(float_features=next_action),
reward=batch.reward,
not_terminal=not_terminal,
step=None,
time_diff=None,
extras=rlt.ExtraData(
mdp_id=None,
sequence_number=None,
action_probability=batch.log_prob.exp(),
max_num_actions=None,
metrics=None,
),
)
def __call__(self, batch):
not_terminal = 1.0 - batch.terminal.float()
action, next_action = one_hot_actions(self.num_actions, batch.action,
batch.next_action,
batch.terminal)
return rlt.DiscreteDqnInput(
state=rlt.FeatureData(float_features=batch.state),
action=action,
next_state=rlt.FeatureData(float_features=batch.next_state),
next_action=next_action,
possible_actions_mask=torch.ones_like(action).float(),
possible_next_actions_mask=torch.ones_like(next_action).float(),
reward=batch.reward,
not_terminal=not_terminal,
step=None,
time_diff=None,
extras=rlt.ExtraData(
mdp_id=None,
sequence_number=None,
action_probability=batch.log_prob.exp(),
max_num_actions=None,
metrics=None,
),
)
def __call__(self, batch):
not_terminal = 1.0 - batch.terminal.float()
# normalize actions
action = rescale_actions(
batch.action,
new_min=self.train_low,
new_max=self.train_high,
prev_min=self.action_low,
prev_max=self.action_high,
)
# only normalize non-terminal
non_terminal_indices = (batch.terminal == 0).squeeze(1)
next_action = torch.zeros_like(action)
next_action[non_terminal_indices] = rescale_actions(
batch.next_action[non_terminal_indices],
new_min=self.train_low,
new_max=self.train_high,
prev_min=self.action_low,
prev_max=self.action_high,
)
dict_batch = {
InputColumn.STATE_FEATURES:
batch.state,
InputColumn.NEXT_STATE_FEATURES:
batch.next_state,
InputColumn.ACTION:
action,
InputColumn.NEXT_ACTION:
next_action,
InputColumn.REWARD:
batch.reward,
InputColumn.NOT_TERMINAL:
not_terminal,
InputColumn.STEP:
None,
InputColumn.TIME_DIFF:
None,
InputColumn.EXTRAS:
rlt.ExtraData(
mdp_id=None,
sequence_number=None,
action_probability=batch.log_prob.exp(),
max_num_actions=None,
metrics=None,
),
}
has_candidate_features = False
try:
dict_batch.update({
InputColumn.CANDIDATE_FEATURES:
batch.doc,
InputColumn.NEXT_CANDIDATE_FEATURES:
batch.next_doc,
})
has_candidate_features = True
except AttributeError:
pass
output = rlt.PolicyNetworkInput.from_dict(dict_batch)
if has_candidate_features:
output.state = rlt._embed_states(output.state)
output.next_state = rlt._embed_states(output.next_state)
return output
def test_seq2slate_eval_data_page(self):
"""
Create 3 slate ranking logs and evaluate using Direct Method, Inverse
Propensity Scores, and Doubly Robust.
The logs are as follows:
state: [1, 0, 0], [0, 1, 0], [0, 0, 1]
indices in logged slates: [3, 2], [3, 2], [3, 2]
model output indices: [2, 3], [3, 2], [2, 3]
logged reward: 4, 5, 7
logged propensities: 0.2, 0.5, 0.4
predicted rewards on logged slates: 2, 4, 6
predicted rewards on model outputted slates: 1, 4, 5
predicted propensities: 0.4, 0.3, 0.7
When eval_greedy=True:
Direct Method uses the predicted rewards on model outputted slates.
Thus the result is expected to be (1 + 4 + 5) / 3
Inverse Propensity Scores would scale the reward by 1.0 / logged propensities
whenever the model output slate matches with the logged slate.
Since only the second log matches with the model output, the IPS result
is expected to be 5 / 0.5 / 3
Doubly Robust is the sum of the direct method result and propensity-scaled
reward difference; the latter is defined as:
1.0 / logged_propensities * (logged reward - predicted reward on logged slate)
* Indicator(model slate == logged slate)
Since only the second logged slate matches with the model outputted slate,
the DR result is expected to be (1 + 4 + 5) / 3 + 1.0 / 0.5 * (5 - 4) / 3
When eval_greedy=False:
Only Inverse Propensity Scores would be accurate. Because it would be too
expensive to compute all possible slates' propensities and predicted rewards
for Direct Method.
The expected IPS = (0.4 / 0.2 * 4 + 0.3 / 0.5 * 5 + 0.7 / 0.4 * 7) / 3
"""
batch_size = 3
state_dim = 3
src_seq_len = 2
tgt_seq_len = 2
candidate_dim = 2
reward_net = FakeSeq2SlateRewardNetwork()
seq2slate_net = FakeSeq2SlateTransformerNet()
src_seq = torch.eye(candidate_dim).repeat(batch_size, 1, 1)
tgt_out_idx = torch.LongTensor([[3, 2], [3, 2], [3, 2]])
tgt_out_seq = src_seq[
torch.arange(batch_size).repeat_interleave(tgt_seq_len),
tgt_out_idx.flatten() - 2, ].reshape(batch_size, tgt_seq_len,
candidate_dim)
ptb = rlt.PreprocessedTrainingBatch(
training_input=rlt.PreprocessedRankingInput(
state=rlt.FeatureData(float_features=torch.eye(state_dim)),
src_seq=rlt.FeatureData(float_features=src_seq),
tgt_out_seq=rlt.FeatureData(float_features=tgt_out_seq),
src_src_mask=torch.ones(batch_size, src_seq_len, src_seq_len),
tgt_out_idx=tgt_out_idx,
tgt_out_probs=torch.tensor([0.2, 0.5, 0.4]),
slate_reward=torch.tensor([4.0, 5.0, 7.0]),
),
extras=rlt.ExtraData(
sequence_number=torch.tensor([0, 0, 0]),
mdp_id=np.array(["0", "1", "2"]),
),
)
edp = EvaluationDataPage.create_from_tensors_seq2slate(
seq2slate_net, reward_net, ptb.training_input, eval_greedy=True)
logger.info(
"---------- Start evaluating eval_greedy=True -----------------")
doubly_robust_estimator = OPEstimatorAdapter(DoublyRobustEstimator())
dm_estimator = OPEstimatorAdapter(DMEstimator())
ips_estimator = OPEstimatorAdapter(IPSEstimator())
switch_estimator = OPEstimatorAdapter(SwitchEstimator())
switch_dr_estimator = OPEstimatorAdapter(SwitchDREstimator())
doubly_robust = doubly_robust_estimator.estimate(edp)
inverse_propensity = ips_estimator.estimate(edp)
direct_method = dm_estimator.estimate(edp)
# Verify that Switch with low exponent is equivalent to IPS
switch_ips = switch_estimator.estimate(edp, exp_base=1)
# Verify that Switch with no candidates is equivalent to DM
switch_dm = switch_estimator.estimate(edp, candidates=0)
# Verify that SwitchDR with low exponent is equivalent to DR
switch_dr_dr = switch_dr_estimator.estimate(edp, exp_base=1)
# Verify that SwitchDR with no candidates is equivalent to DM
switch_dr_dm = switch_dr_estimator.estimate(edp, candidates=0)
logger.info(f"{direct_method}, {inverse_propensity}, {doubly_robust}")
avg_logged_reward = (4 + 5 + 7) / 3
self.assertAlmostEqual(direct_method.raw, (1 + 4 + 5) / 3, delta=1e-6)
self.assertAlmostEqual(direct_method.normalized,
direct_method.raw / avg_logged_reward,
delta=1e-6)
self.assertAlmostEqual(inverse_propensity.raw, 5 / 0.5 / 3, delta=1e-6)
self.assertAlmostEqual(
inverse_propensity.normalized,
inverse_propensity.raw / avg_logged_reward,
delta=1e-6,
)
self.assertAlmostEqual(doubly_robust.raw,
direct_method.raw + 1 / 0.5 * (5 - 4) / 3,
delta=1e-6)
self.assertAlmostEqual(doubly_robust.normalized,
doubly_robust.raw / avg_logged_reward,
delta=1e-6)
self.assertAlmostEqual(switch_ips.raw,
inverse_propensity.raw,
delta=1e-6)
self.assertAlmostEqual(switch_dm.raw, direct_method.raw, delta=1e-6)
self.assertAlmostEqual(switch_dr_dr.raw, doubly_robust.raw, delta=1e-6)
self.assertAlmostEqual(switch_dr_dm.raw, direct_method.raw, delta=1e-6)
logger.info(
"---------- Finish evaluating eval_greedy=True -----------------")
logger.info(
"---------- Start evaluating eval_greedy=False -----------------")
edp = EvaluationDataPage.create_from_tensors_seq2slate(
seq2slate_net, reward_net, ptb.training_input, eval_greedy=False)
doubly_robust_estimator = OPEstimatorAdapter(DoublyRobustEstimator())
dm_estimator = OPEstimatorAdapter(DMEstimator())
ips_estimator = OPEstimatorAdapter(IPSEstimator())
doubly_robust = doubly_robust_estimator.estimate(edp)
inverse_propensity = ips_estimator.estimate(edp)
direct_method = dm_estimator.estimate(edp)
self.assertAlmostEqual(
inverse_propensity.raw,
(0.4 / 0.2 * 4 + 0.3 / 0.5 * 5 + 0.7 / 0.4 * 7) / 3,
delta=1e-6,
)
self.assertAlmostEqual(
inverse_propensity.normalized,
inverse_propensity.raw / avg_logged_reward,
delta=1e-6,
)
logger.info(
"---------- Finish evaluating eval_greedy=False -----------------")
